ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 7. Vol. 28. 2022

DOI: 10.17587/it.28.359-368

L. F. Tagirova, Cand. Ped. Sc., Associate Professor, T. M. Zubkova, Dr. Tech. Sc., Professor, Orenburg State University, Orenburg, Russian Federation

Development of an Expert System for Determining the Status of a Trained Intelligent System Based on the Algorithm of Fuzzy Output Mamdani

Currently, e-learning systems are widely used in the higher education system, leading among which are intelligent learning systems (IOS). A distinctive feature of using such systems is their ability to adapt the educational process to the individual characteristics of students. The article describes the development of an intellectual system that implements the selection of appropriate theoretical material for each student based on an analysis of his status ("expert," "professional," "master," "novice," "trainee"). The status of the trainee is a fuzzy characteristic, reflecting the degree of possession of the course material and consisting of two components: the level of assimilation of the discipline and the formation of the student's personal qualities. As a tool for determining status, it is proposed to use a fuzzy expert system, the core of which is the production rule base. When forming the database of fuzzy products, a description of each linguistic variable specified on the basis of the values of the student's reference model was used. This model includes dominant indicators for each quality, characterizing possible student statuses. During operation of the software, the results of evaluation of trainees "qualities are read from the database and compared with the rules of the expert system. The Mamdani method was used as the fuzzy inference algorithm. As a result, the student is offered a theoretical material of the discipline adapted to his current status. In the course of working with the system, students will be able to study the material of the discipline, building their own learning trajectory. The introduction of the developed IOS, which implements adaptive individual learning paths for each student, will significantly increase the efficiency and quality of training of specialists [1].
Keywords: fuzzy logic, expert system, Mamdani fuzzy inference algorithm, trainee status, intelligent training system

P. 359–368

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